3D point cloud maps are an accumulation of laser scans obtained at different positions and times. Since laser scans represent a snapshot of the surrounding at the time of capture, they often contain moving objects which may not be observed at all times. Dynamic objects in point cloud maps decrease the quality of maps and affect localization accuracy, hence it is important to remove the dynamic objects from 3D point cloud maps. In this paper, we present a robust method to remove dynamic objects from 3D point cloud maps. Given a registered set of 3D point clouds, we build an occupancy map in which the voxels represent the occupancy state of the volume of space over an extended time period. After building the occupancy map, we use it as a filter to remove dynamic points in lidar scans before adding the points to the map. Furthermore, we accelerate the process of building occupancy maps using object detection and a novel voxel traversal method. Once the occupancy map is built, dynamic object removal can run in real-time. Our approach works well on wide urban roads with stopped or moving traffic and the occupancy maps get better with the inclusion of more lidar scans from the same scene.

This paper proposes a robust and reliable motion-based calibration algorithm of multiple LiDARs and INS (in-ertial navigation system). The conventional methods utilize appearance-based matching results or only motions of sensors. Therefore, this paper proposes a graph structure-based optimization method with three special constraints. The first constraint is matching-based constraint. This constraint provides reliable transformations between LiDARs. Second, motion-based constraint allows to calibrate between LiDARs and INS. Last one is rigid body constraint from an assumption of rigid body platform. This constraint significantly improves position errors from ambiguities of monotonous motions, and it makes LiDAR sensors possible to fully calibrate with 6-DoF (degree-of-freedom) motions of INS. The proposed method was analyzed with KITTI data and applied to real autonomous vehicle to show its superiority.